{"id":"W2110785083","doi":"10.1109/tmag.2009.2022952","title":"Multiscale Combined Radial Basis Function Collocation Method for Eddy Currents Analysis in High-Speed Moving Conductors","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Numerical methods in engineering","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Radial basis function; Collocation (remote sensing); Eddy current; Collocation method; Finite element method; Computer science; Basis function; Regularized meshless method; Electrical conductor; Singular boundary method; Mathematical analysis; Physics; Mathematics; Artificial intelligence; Artificial neural network; Boundary element method; Machine learning; Differential equation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002691817,0.0002523821,0.0003899667,0.0006757691,0.00007767867,0.00003640775,0.0001313345,0.0001602668,0.00007297608],"category_scores_gemma":[0.00003149804,0.000297459,0.0001794125,0.00148459,0.00001930995,0.0001369205,6.229955e-7,0.0003096453,0.000006292633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002111215,"about_ca_system_score_gemma":0.00001160333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003651601,"about_ca_topic_score_gemma":0.00003788947,"domain_scores_codex":[0.998657,0.00008780404,0.0004328658,0.0002933197,0.00019791,0.0003310725],"domain_scores_gemma":[0.9991394,0.0003671856,0.00004173749,0.0002793827,0.00006159847,0.0001106972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005102114,0.0001076936,0.00002906617,0.00002368341,0.00009896015,5.008902e-7,0.00008964421,0.8042313,0.01680796,0.000021896,0.00002171859,0.1785165],"study_design_scores_gemma":[0.001274961,0.0003410794,0.003887421,0.00001813303,0.0005209063,6.315034e-7,0.00003839916,0.9655468,0.02767529,0.0002204874,0.0001616375,0.0003142599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05091053,0.00004940714,0.9463869,0.00005372848,0.00177369,0.0004493658,0.0000465132,0.0002946262,0.00003521247],"genre_scores_gemma":[0.730597,0.00003316936,0.2691323,0.00002827542,0.00005220202,0.00004665334,0.00001311847,0.0000340279,0.00006322739],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6796865,"threshold_uncertainty_score":0.9999477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01603100628210836,"score_gpt":0.2786299257832856,"score_spread":0.2625989195011773,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}